Generation Assets Research Articles

Improving the discoverability, accessibility, and citability of omics datasets: a case report.

Although omics datasets represent valuable assets for hypothesis generation, model testing, and data validation, the infrastructure supporting their reuse lacks organization and consistency. Using nuclear receptor signaling transcriptomic datasets as proof of principle, we developed a model to improve the discoverability, accessibility, and citability of published omics datasets. Primary datasets were retrieved from archives, processed to extract data points, then subjected to metadata enrichment and gap filling. The resulting secondary datasets were exposed on responsive web pages to support mining of gene lists, discovery of related datasets, and single-click citation integration with popular reference managers. Automated processes were established to embed digital object identifier-driven links to the secondary datasets in associated journal articles, small molecule and gene-centric databases, and a dataset search engine. Our model creates multiple points of access to reprocessed and reannotated derivative datasets across the digital biomedical research ecosystem, promoting their visibility and usability across disparate research communities.

MERGERS AND ACQUISITIONS-THE GAME OF PROFIT AND LOSS: A STUDY OF INDIAN BANKING SECTOR

INTRODUCTION:The Indian banking sector is a spine of Indian economy. In the last few years, the Indian banking sector has made brisk growth in terms of revenue due to favourable factors, but few banks were not able to perform well. To improve performance, many banks were merged with other banks. Apart from this objective, the merger is to improve banking services, create operational and financial synergy, market share gain, value maximization, market expansion & creation of large identity. Among all this, the matter that needs much concern is how the merger affects the overall financial performance of banks.In 1980, merger and company performance was an important issue in front of management thinkers. An empirical study (Michael Lubatkin, 1983) has made an argument that merger results in improvement of the firm's performance. Studies in 90's have also examined the performance of the firm. (Healy, 1992) has studied the performance of firms using a sample of the 50 largest mergers between U.S. public, industrial firms completed in the period 1979 to 1983. A Study has revealed that after the merger, there was improvement in performance in terms of assets utilization, productivity and long-term investment. (Marcia, 1991) had analysed the post-merger financial performance of largest banks merged during 1982 to 1987& enhanced that after merger, assets growth and employee productivity has improved. Some argue that mergers and acquisitions activities create agency problems, resulting in less than optimal returns (Jensen, 1986) where as others argue that M&A create synergies that result into benefit for firm (Weston et al, 2004).This is a comprehensive review of the merger and firm's performance. Again, there is no systematic literature review of merger and firm's performance which has been measured from different parameters. Given the fact that, the merger and firm's performance has scope for further studies. Thus, there is a need to analyses pre & post-merger impact of merger on financial performance. Research Gap can be seen at various points in present studies where there are scope for further study. So, to fulfil this gap, this present study will address the Comparison of pre & post-merger financial performance using Economic Value Added (EVA). The main objective of this study is to Analyse the comparative position of pre & post-merger financial performance of selected banks.The remainder of this paper is organized as follows. Section 2 explains the theoretical background of different literature on merger and firm's performance. The methodology is presented in Section 3. Empirical evidence and discussion on data analysis is presented in Section 4. Conclusion is presented in section 5.Below given table 1 shows various merger happen in Indian banking industry.RESEARCH QUESTION:RQ. - Does the financial performance of all banks involved in merger gets improve after merger?LITERATURE REVIEW:Many researchers have analysed pre & post-merger performance of merged firm. Researchers from all over the world has taken various industries & carried out research work on merger & firm's performance. The detailed literature reviews are discussed for the merger happened in Canada, Dubai, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, U.K & U.S.A. Some researchers have made an argument that mergers and acquisitions result in negative outcome (Jensen, 1986) where as others argues that M&A improves the firm's performance (Weston et al, 2004). Here, this section contains the Theoretical background on merger and firm's financial performance.Financial performance of firm refers to measurement that how well a firm can able to manage its assets for generation of revenue and profit. There are various measures to study the financial performance such as income, assets, profit and profitability ratios. …

Power Markets with Renewables: New Perspectives for the European Target Model

We discuss at the European example how power market design evolves with increasing shares of intermittent renewables. Short-term markets and system operation have to accommodate for the different needs of renewable and conventional generation assets and flexibility options. This can be achieved by pooling resources over larger geographic areas through common auction platforms, realizing the full flexibility of different assets based on multi-part bids while efficiently allocating scarce network resources. For investment and re-investment choices different technology groups like wind and solar versus fossil fuel based generation may warrant a different treatment - reflecting differing levels of publicly accessible information, requirements for grid infrastructure, types of strategic choices relevant for the sector and shares of capital cost in overall generation costs. We discuss opportunities for such a differentiated treatment while maintaining synergies in short-term system operation.

The value of energy storage in decarbonizing the electricity sector

Electrical energy storage could play an important role in decarbonizing the electricity sector by offering a new, carbon-free source of operational flexibility, improving the utilization of generation assets, and facilitating the integration of variable renewable energy sources. Yet, the future cost of energy storage technologies is uncertain, and the value that they can bring to the system depends on multiple factors. Moreover, the marginal value of storage diminishes as more energy storage capacity is deployed. To explore the potential value of energy storage in deep decarbonization of the electricity sector, we assess the impact of increasing levels of energy storage capacity on both power system operations and investments in generation capacity using a generation capacity expansion model with detailed unit commitment constraints. In a case study of a system with load and renewable resource characteristics from the U.S. state of Texas, we find that energy storage delivers value by increasing the cost-effective penetration of renewable energy, reducing total investments in nuclear power and gas-fired peaking units, and improving the utilization of all installed capacity. However, we find that the value delivered by energy storage with a 2-hour storage capacity only exceeds current technology costs under strict emissions limits, implying that substantial cost reductions in battery storage are needed to justify large-scale deployment. In contrast, storage resources with a 10-hour storage capacity deliver value consistent with the current cost of pumped hydroelectric storage. In general, while energy storage appears essential to enable decarbonization strategies dependent on very high shares of wind and solar energy, storage is not a requisite if a diverse mix of flexible, low-carbon power sources is employed, including flexible nuclear power.

Co-Optimization of Generation Expansion Planning and Electric Vehicles Flexibility

The envisaged de-carbonization of power systems poses unprecedented challenges enhancing the potential of flexible demand. However, the incorporation of the latter in system planning has yet to be comprehensively investigated. This paper proposes a novel planning model that allows co-optimizing the investment and operating costs of conventional generation assets and demand flexibility, in the form of smart-charging/discharging electric vehicles (EV). The model includes a detailed representation of EV operational constraints along with the generation technical characteristics, and accounts for the costs required to enable demand flexibility. Computational tractability is achieved through clustering generation units and EV, which allows massively reducing the number of decision variables and constraints, and avoiding non-linearities. Case studies in the context of the U.K. demonstrate the economic value of EV flexibility in reducing peak demand levels and absorbing wind generation variability, and the dependence of this value on the required enabling cost and users’ traveling patterns.

Economic rebalancing and electricity demand in China

As China undertakes a transition to a more consumption- and service-oriented economy, electricity consumption in the richest provinces has been plateauing. Though projections for electricity use in 2030 range widely from 5308 to 8292kWh per capita, most results suggest that policymakers and power system planners need seriously re-evaluate the need for new generation capacity to avoid overinvestment that could lead to stranded generation assets.

Promoting Large and Closing Small in China�s Coal Power Sector 2006�2013: A CO2 Mitigation Assessment Based on a Vintage Structure

In the period 2006-2013, China's power sector adopted a policy which aimed to add large scale units (larger than 600 MW) and decommission small, old (but still technically functioning) and inefficient power plants in order to accelerate energy saving and mitigate emissions. The early-retirement (ER) of nearly 90 GW of capacity and massive coal power installation in this period significantly altered the pattern of the coal power generation assets. In this paper, it will be shown that the existing structure of coal power installations was built and based on stock, stock efficiency and assumptions on the yearly distribution of early retirement units. With regard to this structure, we measure the of large-scale closure on the aggregated age of power generating plants, efficiency and cumulative CO2 emission. It shows that 0.1 billion tons of CO2 reductions can be obtained as a result of this policy for the period 2006-2050, compared to the counter-factual case, i.e. with no ER during 2006-2013. Instead, 1.2 billion more emissions are to occur if coal power additions are completely banned from 2020 as a stringent coal control policy. The substantial difference is fundamentally induced by the slow turnover nature of long-lived power assets and the younger coal plants in the ER case, which would run for a longer time and result in more emissions. It is necessary to take account of how to avoid the new lock-in effects of ER action intended to unlock existing infrastructures in the context of required climate change mitigation. The early phase-out of inefficient equipment is not necessarily a green investment.

A Full Prussian Blue Cell Containing a Hexacyanomanganate(II/I) Anode and a Cosolvent Electrolyte

The continued deployment of variable generation assets including solar and wind has resulted in widespread concerns regarding electric grid stability.1-2 Energy storage in the form of batteries can smooth the volatile power outputs of solar and wind, alleviating the impact of that volatility on the rest of the grid.2 Unfortunately, existing battery technologies do not offer a long enough cycle life or high enough power to be economically used for this application. We previously showed that copper hexacyanoferrate, a Prussian Blue analogue (PBA) cathode material, can survive for over 40,000 deep discharge, high rate cycles.3 This cathode cycle life was an order of magnitude longer than incumbent batteries under consideration for renewables support. To achieve a significant lifetime advantage in a practical battery however, an anode having comparable cycle life was needed. In this presentation, we describe a new Na+cell containing PBA as both the cathode and the anode and a nonflammable aqueous-organic cosolvent electrolyte. By using a PBA as the active material for both electrodes, neither electrode limits the lifetime or kinetics of the cell. In this cell, a transition metal hexacyanoferrate cathode is paired with a new manganese(II) hexacyanomanganate(II/I) (MnHCMn) anode. Soft X-ray absorbance spectroscopy (sXAS) shows that the MnHCMn anode operates by a novel Mn(II/I) redox in which low-spin 3d6 MnI(CN)6 complexes are formed during charging (reduction). Pairing this anode with a hexacyanoferrate-based cathode results in a 1.6 V average cell voltage, which is nearly twice the 950 mV cell voltage of previously reported full PBA cells.4 Lab-scale full pouch cells containing have a hexacyanomanganate(II/I)-based anode and a hexacyanoferrate-based cathode have an average projected lifetime of over 30,000 cycles at 100% depth of discharge based on >2,000 cycles and six months of testing. This long cycle life is achieved because both of the electrodes cycle by near-zero strain single-phase insertion reactions. The addition of organic cosolvents to the aqueous electrolyte prevents dissolution of the PBAs while retaining the high Na+ conductivity and nonflammability of aqueous electrolytes. This results in an extension of the projected cell calendar life from just months to years. Finally, the rapid kinetics of the PBA electrodes and cosolvent electrolyte allow access to 80% of total cell energy above 1.15 V during 12C discharge. The long cycle life and high power capability of these cells make them particularly attractive for short-duration renewables support applications such as solar intermittency smoothing.5

Local matching of flexible load in smart grids

Today’s power systems are experiencing a transition from primarily fossil fuel based generation toward greater shares of renewable energy sources. It becomes increasingly costly to manage the resulting uncertainty and variability in power system operations solely through flexible generation assets. Incorporating demand side flexibility through appropriately designed incentive structures can add an additional lever to balance demand and supply. Based on a supply model using empirical wind generation data and a discrete model of flexible demand with temporal constraints, we design and evaluate a local online market mechanism for matching flexible load and uncertain supply. Under this mechanism, truthful reporting of flexibility is a dominant strategy for consumers reducing payments and increasing the likelihood of allocation. Suppliers, during periods of scarce supply, benefit from elevated critical-value payments as a result of flexibility-induced competition on the demand side. We find that, for a wide range of the key parameters (supply capacity, flexibility level), the cost of ensuring incentive compatibility in a smart grid market, relative to the welfare-optimal matching, is relatively small. This suggests that local matching of demand and supply can be organized in a decentral manner in the presence of a sufficiently flexible demand side. Extending the stylized demand model to include complementary demand structures, we demonstrate that decentral matching induces only minor efficiency losses if demand is sufficiently flexible. Furthermore, by accounting for physical grid limitations we show that flexibility and grid capacity exhibit complementary characteristics.

Impact of the Carbon Price on Australia's Electricity Demand, Supply and Emissions

Australia’s carbon price has been in operation for two years. The electricity sector accounts for the majority of emissions covered under the scheme. This paper examines the impact of the carbon price on the electricity sector between 1 July 2012 and 30 June 2014, focusing on the National Electricity Market (NEM). Over this period, electricity demand in the NEM declined by 3.8 per cent, the emissions intensity of electricity supply by 4.6 per cent, and overall emissions by 8.2 per cent, compared to the two-year period before the carbon price. We detail observable changes in power demand and supply mix, and estimate the quantitative effect of the effect of the carbon price. We estimate that the carbon price led to an average 10 per cent increase in nominal retail household electricity prices, an average 15 per cent increase in industrial electricity prices and a 59 per cent increase in wholesale (spot) electricity prices. It is likely that in response, households, businesses and the industrial sector reduced their electricity use. We estimate the demand reduction attributable to the carbon price at 2.5 to 4.2 TWh per year, about 1.3 to 2.3 per cent of total electricity demand in the NEM. The carbon price markedly changed relative costs between different types of power plants. Emissions-intensive brown coal and black coal generators reduced output and 4GW of emissions-intensive generation capacity was taken offline. We estimate that these shifts in the supply mix resulted in a 16 to 28kg CO2/MWh reduction in the emissions intensity of power supply in the NEM, a reduction between 1.8 and 3.3 per cent. The combined impact attributable to the carbon price is estimated as a reduction of between 5 and 8 million tonnes of CO2emissions (3.2 to 5 per cent) in 2012/13 and between 6 and 9 million tonnes (3.5 to 5.6 per cent) in 2013/14, and between 11 and 17 million tonnes cumulatively. There are fundamental difficulties in attributing observed changes in demand and supply to specific causes, especially over the short term, and in this light we use conservative parameters in the estimation of the effect of the carbon price. We conclude that the carbon price has worked as expected in terms of its short-term impacts. However, its effect on investment in power generation assets has probably been limited, because of policy uncertainty about the continuation of the carbon pricing mechanism. For emissions pricing to have its full effect, a stable, long-term policy framework is needed.

Optimizing industries’ power generation assets on the electricity markets

For historical reasons, many large industrial sites have their own power generation units, either because the site was isolated when it was built or because the local network was not reliable enough to ensure regular production. This can apply to energy-intensive industries like refineries or LNG plants in the Oil & Gas sector, but also to mining plants, metal industries and chemical plants. These generation assets are usually operated in a suboptimal way, the only concern being the safety of the process. The focus of this work is to determine how industrial plant operators can make optimal use of these assets, considering interactions with the electricity markets.Based on the mathematical description of this optimization problem, a model achieving a double target is introduced. First, to develop a standard architecture that can be easily translated from one case to another. Then, to assess the maximum expected savings and the induced strategy. This model is applied to a refinery case study, where expected earnings are as much as €8M per year and account for 1% of turnover. The figures obtained show that this is a relevant business concept. Following this trend, the potential to implement Waste Heat Recovery in energy-intensive cement plants is considered.

Demand response governed swarm intelligent grid scheduling framework for social welfare

Abstract Peak load defines the generation, transmission and distribution capacity of interconnected power network. As load changes throughout the day and the year, electricity systems must be able to deliver the maximum load at all times, which will be hard trade for a practical power network. Smart grid technologies show strong potential to optimize asset utilization by shifting peak load to off peak times, thereby decoupling the electricity growth from peak load growth. Under Smart grid trade regulation, with continuous varying demand pattern, electricity price will be uneven as well. On this view point, in order to obtain a flatten demand, without affecting the welfare of the market participants, this paper presents an on-going effort to develop Demand Response (DR) governed swarm intelligence based stochastic peak load modeling methodology capable of restoring the market equilibrium during price and demand oscillations of the real-time smart power networks. This proposed DR based methodology allows generators and loads to interact in an automated fashion in real time, coordinating demand to flatten spikes and thereby minimizing erratic variations of price of electricity. For proper utilization of DR connectivity, a Curtailment Limiting Index (CLI) has been formulated, monitoring which in real time, for each of the Load Dispatch Centers (LDCs), the system operator can shape the electricity demand according to the available capacity of generation, transmission and distribution assets. The proposed methodology can also be highlighted for generating the most economical schedule for social welfare with standard operational status in terms of voltage profile, system loss and optimal load curtailment. The case study has been carried out in IEEE 30 bus scenario as well as on a practical 203 bus-265 line power network (Indian Eastern Grid) with both generator characteristics and price responsive demand characteristics or DR as inputs and illustrious Particle Swarm Optimization (PSO) technique has assisted the fusion of the proposed model and methodology. Encouraging simulation results suggest that, the effective deployment of this methodology may lead to an operating condition where an overall benefit of all the power market participants with standard operational status can be ensured and the misuse of electricity will be minimized.

Electricity and Natural Gas Interdependency: Comparison of Two Methodologies for Coupling Large Market Models Within the European Regulatory Framework

Power generation growth based on natural gas fired power plants (NGFPPs) has lead to increasing interactions between electric power and natural gas industries. More companies are progressively and simultaneously participating as big players in both markets. However, each company has traditionally been settled in one side, holding a particular competitive advantage: electric power generation companies mainly know how to operate their generation assets, whereas gas companies mainly know how to manage their gas supply contracts and make use of often regulated gas assets. Multi-product energy companies have even created independent departments which decisions are usually taken uncoordinatedly. In any case, companies (or departments) usually support their decision-making process in mathematical tools which represent each market with detail. This paper presents two methodologies for coupling two interdependent electricity and gas market models formulated as optimization problems. Each methodology fulfills different department wishes. The “electricity-perspective” methodology maximizes electricity market profits after calculating equivalent gas contracts with the gas market model. In contrast, the “gas-perspective” methodology minimizes gas operation costs after obtaining the relationship between the marginal revenue and the gas consumption with the electricity market model. This coordinated solution would allow companies to obtain synergies, resulting in a competitive advantage over other companies that operate uncoordinatedly in both markets

Optimal Management of Power Generation Assets: Interaction with the Electricity Markets

Abstract For historical reasons, many large industrial sites have their own power generation units, whether it is because the site was isolated when built up or the local network wasn’t reliable enough to ensure a regular production. This can apply for energy-intensive industries like refineries or LNG plants in the Oil & Gas sector, but also mining plants, metal industries or chemical plants for instance. These generation assets are usually operated in a suboptimal way, the only concern being the safety of the process. The gist of this work will be to determine an optimal use of these assets for the industrial plant operator, considering interactions with the electricity markets. Laying on the mathematical description of this optimization problem, a model was built up following a double target. First, to develop a standard architecture that would easily be translated from one case to the other. Then, to assess how high the expected savings could go and what would the induced strategy be. This model has been applied to a refinery case study, where the expected earnings rose up to 8 M€ per year. It accounts for 1% of the turnover. The figures obtained show that this is a relevant business concept. Following this trend, other energy-intensive case studies are being considered, from cement plant to paper plant.

Climate Policy Uncertainty and Power Generation Investments: A Real Options-CVaR Portfolio Optimization Approach

A decision support framework has been provided to assist investors with long-term decision-making for investment choices in power generation assets under uncertain climate policy. The model combines real options analysis and modern portfolio optimization theory. A long-term correlation between carbon and renewable portfolio standard certificate prices is used to model the interaction of climate policies, with a case study being developed to investigate the optimal choice of capacity additions to an existing mix of power generation assets in Australia. The findings show that there is potential for investors to fully hedge their existing fossil fuel based generation assets through the addition of on-shore wind capacity. The model developed allows for (1) the investigation of investment risk and return under uncertain climate policies, and (2) the study of interaction among green policies.

Microgrid investment under uncertainty: a real option approach using closed form contingent analysis

The traditional net present value approach to investment in microgrid assets does not take into account the inherent uncertainties in fuel prices, cost of technology, and microgrid load profile. We propose a real option approach to microgrid investment, which includes solar photovoltaic (PV) and gas-fired generation assets. Likewise the (n, m) exchange literature in real option analysis, we examine cases with interdependency and independency of fuel price and the cost of PV technology. This work, however, makes a major contribution by the way of introducing a new parameter, which is defined as the elasticity of the option value to prices and is used in the formulation of closed form solutions. We further extend the (1, 1) exchange problem here to include operational flexibility of microgrid, such that optimal switching between investment, suspension and re-activation can be examined.

Gas turbine evaporative cooling evaluation for Lagos – Nigeria

Evaporative cooling is widely recognised as one of the available gas turbine power augmentation tools. Due to its inherent characteristics evaporative cooling is thought as being only effective in geographic areas with high ambient air dry bulb temperature and low relative humidity. This paper clarifies, via literature review and thermal modelling examples, the fact that there is still potential for evaporative cooling in high temperature and relative humidity areas. The paper also estimates the maximum cost of the evaporative cooling installation that will yield a positive return of investment to plant owners in the decision of retrofitting evaporative cooling systems for the particular location of Lagos in Nigeria. In consequence power plant operators, especially those whose power generation assets are located on coastal environments, should be aware of the prospective benefits associated with such cooling technology.

Solar ABS: Understanding Market Potential

This article is a summary of the comments made by seven members of a panel titled “Solar ABS: Understanding Market Potential,” held on Tuesday, February 10 at the ABS Vegas 2015 conference. The panel was organized to present varying industry-expert perspectives from solar development firms, securities issuers, investors, attorneys, accountants, and rating agencies on their outlook for a more vibrant and robust securitization market in solar asset-backed securities (ABS). The year 2014 was a banner year with substantial installed capacity growth and the second securitized debt issuance from SolarCity. Electricity-generating solar assets are becoming competitive to mainstream energy generation and attracting interest from institutional investors as an investible asset class. Solar panel prices and soft costs are widely expected to continue to decrease, as firms advance in raw material sourcing and gain better controls over their supply chains and as engineering efficiencies continue to improve. The solar industry now has the benefit of analyzing and observing project portfolios with six to seven years of consistent performance data. The rating agencies continue their work in developing the appropriate rating methodologies and stress-case scenarios for solar securities. The risks can be broadly divided into two categories: technical performance of the solar generation assets and default and renegotiation of contracts with off-takers. Although much attention has been paid to residential solar systems, solar developers have also been penetrating the commercial and industrial market at a remarkable rate. <b>TOPICS:</b>Asset-backed securities (ABS), ESG investing

Dynamic Portfolio Selection Methods for Power Generation Assets

In this paper we start off by reviewing the literature on how to extend the mean-variance portfolio model to multi-stage portfolio problems. We then apply a multi-period portfolio selection model to power generation assets, which is based on a reallocation methodology with scenario tree. Two solution approaches are used: the multi-period rebalancing model and the global solution one. These approaches are contrasted with the efficient frontier obtained for a "buy-and-hold policy", thus helping to illustrate the effect of portfolio dynamization. The study covers all major electricity generation technologies in Germany and investigates the impact of offshore wind and solar power plants on existing power generation portfolios. We find that solar power technology has a positive impact on the efficiency of the portfolios, and the analysis underlines the advantages of using a multi-period rebalancing model for decision-making.

Power Market Design Beyond 2020: Time to Revisit Key Elements?

We revisit key elements of European power market design with respect to both short term operation and longer-term investment and re-investment choices. For short term markets, the European policy debate focuses on the definition of common interfaces, like for example gate closure time. We argue that that this is insufficient if the market design is to accommodate for the different needs of renewable and conventional generation assets and different flexibility options. The market design needs to ensure resources are pooled over larger geographic areas, the full flexibility of different assets can be realized with complex bids and scarce network resources are efficiently used. For investment and re-investment choices we argue that different technology groups like wind and solar versus fossil fuel based generation may warrant different treatment – reflecting different level of publicly accessible information, requirements for grid infrastructure, types of strategic choices relevant for the sector and share of capital cost in overall generation costs. We discuss opportunities for such a differentiated treatment and implications for electricity consumers.